The contour extraction of a moving object, especially of a deforming object, is a challenge in the field of computer vision. In actual applications, for example, in the medial field, the contour extraction of an organ or a part of an organ from a three-dimensional image time series acquired by a computed tomography (CT) apparatus, a Magnetic Resonance Imaging (MRI) apparatus, an ultrasonic (UL) apparatus and the like is beneficial to subsequent measurement on various parameters of the organ.
Some conventional moving object contour extraction methods extract the contour of a moving object separately from each phase, which may lead to an error extraction in a specific phase.
Some other motion tracking based methods which track the contour of a moving object in a motion period of the moving object may produce an error accumulation, resulting in a significant difference between the acquired contour in the first phase and that in the last phase.
In addition, in the field of cardiology, a nuclear magnetic resonance imaging technology is typically used to provide a three-dimensional image time series (3D+T) of a heart. Doctors are highly interested in recognizing a ventricle, an endocardium, an epicardium and analyzing the motion of a heart. The contours of the recognized ventricle, endocardium and epicardium can be used to measure a ventricular blood volume (ejection fraction), the motion of a ventricular wall, a characteristic of wall thickness and the like at different stages of a cardiac cycle. The motion vector of a myocardium can be used to calculate parameters of the myocardium, such as strain and strain force. Left ventricle (LV) is of great importance because it pumps oxygenated blood to various issues of a body from the heart.
There have been developed many medical motion image processing techniques to quantify myocardial motion, including spot tracking, myocardial tagging, registering and propagation contours with initial contour of myocardium, and various myocardium segmentation methods.